Discriminative Input Stream Combination for Conditional Random Field Phone Recognition

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Deep Learning: Methods and Applications (2014, background): “Many of the discriminative techniques for supervised learning in signal and information processing are shallow architectures such as HMMs [52, 127, 147, 186, 188, 290, 394, 418] and conditional random fields (CRFs) [151, 155, 281, 400, 429, 446].”
A tutorial survey of architectures, algorithms, and applications for deep learning (2014, background): “This difficulty is partially responsible for steering away most of the machine learning and signal-processing research from neural networks to shallow models that have convex loss functions (e.g., SVMs, CRFs, and MaxEnt models), for which global optimum can be efficiently obtained at the cost of les”
Machine Learning Paradigms for Speech Recognition: An Overview (2013, methodology): “For example, when applying (26) to Markov random fields, we obtain the training objective of conditional random fields (CRFs) [70]: (27) The partition function is a normalization factor. is a weight vector and is a vector of feature functions referred to as a feature vector.”
Backpropagation training for multilayer conditional random field based phone recognition (2010, methodology): “We intend to explore the use of the ML-CRF systems as feature combiners, since it has been previously shown [12] that dimensionality reduction and a non-linear transformation are important when using multiple feature streams for phone recognition.”